# A neurofeedback app¶

Let’s apply what we learned and build a simple alpha neurofeedback app.

Tip

This use case assumes a little bit of background in EEG processing. If this is not the case for you, focus on the data flow rather than the processing itself, and make sure that you understand the syntax.

Note

This example does not include the sound generation. This can be accomplished with any multimedia programming software that understand the Open Sound Control protocol, such as Pure Data or Max.

The application consists of three graphs. In the main one, we assume that the EEG data is acquired through a LSL inlet. Data is accumulated into a rolling window, on which the classical Welch’s method is applied with default parameters. The frequency bands are then extracted from the periodogram. Finally, the relative alpha power is sent to an OSC outlet. An external application receives this data and plays a sound when the feedback signal crosses a defined threshold. The other two graphs are not strictly required, but illustrate some important principles. The graph containing the Broker node acts as a proxy. It receives data from publishers (in our example, the raw EEG stream and the computed frequency bands) and redistributes it to subscribers. In the last graph, these two data streams are aggregated and saved to a HDF5 file.

Schematic representation of a basic neurofeedback application. Each blue box is a DAG. Together, they constitute the Timeflux application. The white boxes are core nodes. The green boxes are plugin nodes. Yellow boxes indicate external components.

The whole application is expressed in YAML as follows:

graphs:

# The publish/subscribe broker graph
- id: PubSubBroker
nodes:
# Allow communication between graphs
- id: Broker
module: timeflux.nodes.zmq
class: Broker

# The main processing graph
- id: Processing
nodes:
# Receive EEG signal from the network
- id: LSL
module: timeflux.nodes.lsl
params:
name: signal
# Continuously buffer the signal
- id: Rolling
module: timeflux.nodes.window
class: Window
params:
length: 1.5
step: 0.5
# Compute the power spectral density
- id: Welch
module: timeflux_dsp.nodes.spectral
class: Welch
# Average the power over band frequencies
- id: Bands
module: timeflux_dsp.nodes.spectral
class: Bands
# Send to an external application
- id: OSC
module: timeflux.nodes.osc
class: Client
params:
# Publish the raw EEG signal
- id: PublisherRaw
module: timeflux.nodes.zmq
class: Pub
params:
topic: raw
# Publish the frequency bands
- id: PublisherBands
module: timeflux.nodes.zmq
class: Pub
params:
topic: bands
# Connect nodes
edges:
- source: LSL
target: Rolling
- source: Rolling
target: Welch
- source: Welch
target: Bands
- source: Bands:alpha
target: OSC
- source: LSL
target: PublisherRaw
- source: Bands
target: PublisherBands
# Run this graph 25 times per second
rate: 25

# The recorder graph
- id: SaveToFile
nodes:
# Receive data streams from the broker
- id: Subscriber
module: timeflux.nodes.zmq
class: Sub
params:
topics:
- raw
- bands
# Record to file
- id: Recorder
module: timeflux.nodes.hdf5
class: Save
# Connect nodes
edges:
- source: Subscriber:raw
target: Recorder:eeg_raw
- source: Subscriber:bands
target: Recorder:eeg_bands
# Update file every second
rate: 1